What is Data Cleaning in Data Science?

What is Data Cleaning in Data Science?
📌 Introduction
Data Cleaning (or Data Preprocessing) is the process of detecting, correcting, or removing inaccurate, incomplete, or irrelevant data from datasets. It is a crucial step in data science and machine learning, as poor-quality data can lead to incorrect insights and inaccurate predictions. Data Science Course in Pune

 
💡 Fact: 80% of a data scientist’s time is spent cleaning and preparing data!

🛠️ Why is Data Cleaning Important?
🔹 Improves Data Accuracy – Reduces errors and inconsistencies
🔹 Enhances Model Performance – Clean data leads to better predictions
🔹 Prevents Bias – Eliminates duplicate or misleading records
🔹 Ensures Data Consistency – Standardizes formats and missing values

📌 Example:
Imagine a company analyzing customer transactions. If the dataset contains missing prices, incorrect dates, or duplicate entries, the sales analysis will be flawed.

🔍 Key Steps in Data Cleaning
1️⃣ Handling Missing Data
✅ Techniques to fill missing values:

Drop missing values (if the dataset is large)
Fill with mean/median/mode (for numerical data)
Use forward or backward fill (for time-series data)
📌 Example in Python:

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import pandas as pd  
df.fillna(df.mean(), inplace=True)  # Fill missing values with mean  
2️⃣ Removing Duplicates
✅ Duplicates can skew analysis and lead to incorrect conclusions.
📌 Example in Python:
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df.drop_duplicates(inplace=True)  
3️⃣ Standardizing Data Formats
✅ Ensure uniform formats for:

Date formats (YYYY-MM-DD vs. MM/DD/YYYY)
Text cases (uppercase/lowercase)
Units of measurement (e.g., km vs. miles)
📌 Example in Python:

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df['date_column'] = pd.to_datetime(df['date_column'])  # Standardize date format
df['name'] = df['name'].str.lower()  # Convert text to lowercase
4️⃣ Handling Outliers
✅ Outliers can distort analysis and affect ML models.
🔹 Techniques:

Remove extreme values using IQR (Interquartile Range)
Use log transformations to normalize skewed data
📌 Example in Python:

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Q1 = df['column'].quantile(0.25)  
Q3 = df['column'].quantile(0.75)  
IQR = Q3 - Q1  
df = df[(df['column'] >= (Q1 - 1.5 * IQR)) & (df['column'] <= (Q3 + 1.5 * IQR))]  
5️⃣ Correcting Data Entry Errors
✅ Common issues:

Typos (e.g., "USA" vs. "U.S.A")
Inconsistent naming ("Male" vs. "M")
Incorrect spellings
📌 Example in Python:

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df['country'] = df['country'].replace({'U.S.A': 'USA', 'United States': 'USA'})
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